Abstract
Many state-of-the-art face recognition algorithms use image descriptors based on features known as Local Binary Patterns (LBPs). While many variations of LBP exist, so far none of them can automatically adapt to the training data. We introduce and analyze a novel generalization of LBP that learns the most discriminative LBP-like features for each facial region in a supervised manner. Since the proposed method is based on Decision Trees, we call it Decision Tree Local Binary Patterns or DT-LBPs. Tests on standard face recognition datasets show the superiority of DT-LBP with respect of several state-of-the-art feature descriptors regularly used in face recognition applications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ahonen, T., Pietikäinen, M.: Image description using joint distribution of filter bank responses. Pattern Recognition Letters 30, 368–376 (2009)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 2037–2041 (2006)
Albiol, A., Monzo, D., Martin, A., Sastre, J., Albiol, A.: Face recognition using HOG-EBGM. Pattern Recognition Letters 29, 1537–1543 (2008)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding 110, 346–359 (2008)
Bicego, M., Lagorio, A., Grosso, E., Tistarelli, M.: On the use of SIFT features for face authentication. In: CVPR (2006)
Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: ICCV (2007)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Dreuw, P., Steingrube, P., Hanselmann, H., Ney, H.: SURF-face: Face recognition under viewpoint consistency constraints. In: BMVC (2009)
Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D.: The CAS-PEAL large-scale chinese face database and baseline evaluations. IEEE Transactions on System Man, and Cybernetics (Part A) 38, 149–161 (2008)
Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognition 42, 425–436 (2009)
Lei, Z., Li, S.Z., Chu, R., Zhu, X.: Face recognition with local gabor textons. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 49–57. Springer, Heidelberg (2007)
Liao, S., Chung, A.C.S.: Face recognition by using elongated local binary patterns with average maximum distance gradient magnitude. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 672–679. Springer, Heidelberg (2007)
Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning multi-scale block local binary patterns for face recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 828–837. Springer, Heidelberg (2007)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Meng, X., Shan, S., Chen, X., Gao, W.: Local Visual Primitives (LVP) for face modelling and recognition. In: ICPR (2006)
Moosmann, F., Nowak, E., Jurie, F.: Randomized clustering forests for image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 1632–1646 (2008)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29, 51–59 (1996)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for Face-Recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1090–1104 (2000)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Ruiz-del-Solar, J., Verschae, R., Correa, M.: Recognition of faces in unconstrained environments: A comparative study. EURASIP Journal on Advances in Signal Processing 2009, 1–20 (2009)
Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification. In: Proc. Second IEEE Workshop on Applications of Computer Vision, pp. 138–142 (1994)
Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: CVPR (2008)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing 19, 1635–1650 (2010)
Wolf, L., Hassner, T., Taigman, Y.: Descriptor based methods in the wild. In: Real-Life Images Workshop at ECCV (2008)
Wright, J., Hua, G.: Implicit elastic matching with random projections for pose-variant face recognition. In: CVPR, pp. 1502–1509 (2009)
Xie, S., Shan, S., Chen, X., Gao, W.: V-LGBP: Volume based Local Gabor Binary Patterns for face representation and recognition. In: ICPR (2008)
Xie, S., Shan, S., Chen, X., Meng, X., Gao, W.: Learned local Gabor patterns for face representation and recognition. Signal Processing 89, 2333–2344 (2009)
Zhang, B., Shan, S., Chen, X., Gao, W.: Histogram of Gabor phase patterns (HGPP): A novel object representation approach for face recognition. IEEE Transactions on Image Processing 16, 57–68 (2007)
Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A novel non-statistical model for face representation and recognition. In: ICCV (2005)
Zou, J., Ji, Q., Nagy, G.: A comparative study of local matching approach for face recognition. IEEE Transactions on Image Processing 16, 2617–2628 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Maturana, D., Mery, D., Soto, Á. (2011). Face Recognition with Decision Tree-Based Local Binary Patterns. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19282-1_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-19282-1_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19281-4
Online ISBN: 978-3-642-19282-1
eBook Packages: Computer ScienceComputer Science (R0)